Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73650.7365 R Square 0.54240.5424 Adjusted R Square 0.52250.5225 Standard Error 2124.60962124.6096 Observations 4949 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 246,127,958.1791246,127,958.1791 123,063,979.0896123,063,979.0896 27.262927.2629 1.6E-081.6E-08 Residual 4646 207,642,442.8821207,642,442.8821 4,513,966.14964,513,966.1496 Total 4848 453,770,401.0612453,770,401.0612 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14256.268814256.2688 2,513.30952,513.3095 5.67235.6723 0.0000008950.000000895 9197.23929197.2392 19,315.298419,315.2984 Education (Years) 2353.85412353.8541 336.0719336.0719 7.00407.0040 0.0000000090.000000009 1677.37651677.3765 3030.33173030.3317 Experience (Years) 832.8371832.8371 390.1917390.1917 2.13442.1344 0.0381716160.038171616 47.421947.4219 1618.25231618.2523 Step 2 of 2 : How much would you expect your salary to increase if you had one more year of education?
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73650.7365 R Square 0.54240.5424 Adjusted R Square 0.52250.5225 Standard Error 2124.60962124.6096 Observations 4949 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 246,127,958.1791246,127,958.1791 123,063,979.0896123,063,979.0896 27.262927.2629 1.6E-081.6E-08 Residual 4646 207,642,442.8821207,642,442.8821 4,513,966.14964,513,966.1496 Total 4848 453,770,401.0612453,770,401.0612 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14256.268814256.2688 2,513.30952,513.3095 5.67235.6723 0.0000008950.000000895 9197.23929197.2392 19,315.298419,315.2984 Education (Years) 2353.85412353.8541 336.0719336.0719 7.00407.0040 0.0000000090.000000009 1677.37651677.3765 3030.33173030.3317 Experience (Years) 832.8371832.8371 390.1917390.1917 2.13442.1344 0.0381716160.038171616 47.421947.4219 1618.25231618.2523 Step 2 of 2 : How much would you expect your salary to increase if you had one more year of education?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience.
Regression Statistics
Multiple R | 0.73650.7365 |
---|---|
R Square | 0.54240.5424 |
Adjusted R Square | 0.52250.5225 |
Standard Error | 2124.60962124.6096 |
Observations | 4949 |
ANOVA
dfdf | SSSS | MSMS | F� | Significance F� | |
---|---|---|---|---|---|
Regression | 22 | 246,127,958.1791246,127,958.1791 | 123,063,979.0896123,063,979.0896 | 27.262927.2629 | 1.6E-081.6E-08 |
Residual | 4646 | 207,642,442.8821207,642,442.8821 | 4,513,966.14964,513,966.1496 | ||
Total | 4848 | 453,770,401.0612453,770,401.0612 |
Coefficients | Standard Error | t� Stat | P-value | Lower 95%95% | Upper 95%95% | |
---|---|---|---|---|---|---|
Intercept | 14256.268814256.2688 | 2,513.30952,513.3095 | 5.67235.6723 | 0.0000008950.000000895 | 9197.23929197.2392 | 19,315.298419,315.2984 |
Education (Years) | 2353.85412353.8541 | 336.0719336.0719 | 7.00407.0040 | 0.0000000090.000000009 | 1677.37651677.3765 | 3030.33173030.3317 |
Experience (Years) | 832.8371832.8371 | 390.1917390.1917 | 2.13442.1344 | 0.0381716160.038171616 | 47.421947.4219 | 1618.25231618.2523 |
Step 2 of 2 :
How much would you expect your salary to increase if you had one more year of education?
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